Paper Info
Title
Prediction models for dynamic demand response: Requirements, challenges, and insights
Abstract
As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days' worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with D2R.
Year
DOI
Venue
2015
10.1109/SmartGridComm.2015.7436323
2015 IEEE International Conference on Smart Grid Communications (SmartGridComm)
Keywords
Field
DocType
Big Data,independent service operator,short-term time series,data granularity,large building,small residential customer,electricity consumption short-term prediction feasibility analysis,D2R process,curtailment estimation,context vis-a-vis demand prediction,DR,dynamic curtailment program,smart grid,dynamic demand response
Load management,Data modeling,Predictability,Small data,Smart grid,Demand response,Real-time computing,Dynamic demand,Engineering,Big data
Conference
ISSN
Citations 
PageRank 
2373-6836
7
0.56
References 
Authors
16
6
Name
Order
Citations
PageRank
Saima Aman122718.13
Marc Frîncu2102.67
Charalampos Chelmis315627.09
Muhammad Usman Noor470.56
Yogesh Simmhan51904134.15
Viktor K. Prasanna67211762.74